• DOI:10.1021/acs.jctc.5b00301
• Corpus ID: 2055170

Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.

@article{Bereau2015TransferableAM,
  title={Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules.},
  author={T. Bereau and Denis Andrienko and O. Anatole von Lilienfeld},
  journal={Journal of chemical theory and computation},
  year={2015},
  volume={11 7},
  pages={
          3225-33
        }
}

• T. Bereau, D. Andrienko, O. A. von Lilienfeld
• Published 18 March 2015
• Chemistry
• Journal of chemical theory and computation

Accurate representation of the molecular electrostatic potential, which is often expanded in distributed multipole moments, is crucial for an efficient evaluation of intermolecular interactions. Here we introduce a machine learning model for multipole coefficients of atom types H, C, O, N, S, F, and Cl in any molecular conformation. The model is trained on quantum-chemical results for atoms in varying chemical environments drawn from thousands of organic molecules. Multipoles in systems with neutral, cationic, and anionic molecular charge states are treated with individual models. The models' predictive accuracy and applicability are illustrated by evaluating intermolecular interaction energies of nearly 1,000 dimers and the cohesive energy of the benzene crystal.